Skip to content

Instantly share code, notes, and snippets.

@wojteklu
wojteklu / clean_code.md
Last active October 20, 2026 08:05
Summary of 'Clean code' by Robert C. Martin

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.


General rules

  1. Follow standard conventions.
  2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
  3. Boy scout rule. Leave the campground cleaner than you found it.
  4. Always find root cause. Always look for the root cause of a problem.

Design rules

@xPaw
xPaw / steam_quick_queue.user.js
Last active October 20, 2026 08:03
⚠ This script has been integrated into SteamDB browser extension!
// ==UserScript==
// @name Steam Queue Auto Discoverer
// @description Discover the Steam queue three times to get the sale cards
// @version 2.3.0
// @namespace https://gist.github.com/xPaw/73f8ae2031b4e528abf7
// @icon https://store.steampowered.com/favicon.ico
// @match https://store.steampowered.com/explore*
// @grant none
// ==/UserScript==
@BjoernSchilberg
BjoernSchilberg / README.md
Last active October 20, 2026 07:58
Geodaten Geodata OpenData

Geodaten

TopPlusOpen

Das BKG hat ein Kartenproduk erstellt, das in den Bundesländern, in welchen es Open-Data-Datensätze gibt, diese verwendet und in allen anderen Bundesländern auf OSM zurückgreift.

Das BKG als Bundesbehörde hat nur die Rechte an kleinmaßstäblichen Daten (1:200.000 und kleiner), nicht jedoch an den großmaßstäblichen Daten.

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

<!DOCTYPE html>
<html>
<head>
<title></title>
<meta charset="utf-8" />
<script src="Scripts/jquery-1.9.1.min.js"></script>
<link href="Content/bootstrap.min.css" rel="stylesheet" />
<script src="Scripts/isRockFx.js"></script>
</head>
<body>
@acmerfight
acmerfight / .gitignore
Last active October 20, 2026 07:41
macOS WeChat 4.x local toolkit: reuse verified keys first; one-time temporary-copy bootstrap; DB+WAL read, media and verified UI send; root EPERM findings
.wechat-local/
.wechat-ui-state/
private*/
*.png
*.db
*.db-wal
*.db-shm
keys*.json
*state*.json
*.app/
@joshp123
joshp123 / SKILL.md
Created October 19, 2026 08:24
/better: an agent skill that asks for a deeper version of a task, not a bigger one. Drop into ~/.claude/skills/better/ or ~/.agents/skills/better/
name better
description Push the current task, or a prompt the user gives, to a deeper version of itself. Use only when the user explicitly types /better or $better.

I want a deeper version of this task, not a bigger one. Before rewriting it: (1) say what a top professional in this field would do here that I'm not asking for; (2) name the rawest material they would start from instead of what has already been digested for me; (3) name the specific methods and tools they would use; (4) say what, other than you or me, would show the result is right. Then rewrite my prompt to ask for exactly that.

How to apply it